• Journal of the Korean Statistical Society
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• 제12권2호
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• pp.81-90
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• 1983

The least squares estimator of disturbance variance in a regression model is biased under a serial correlation. Under the assumption of an AR(I), Theil(1971) crudely related the bias with the autocorrelation of the disturbances and the autocorrelation of the explanatory variable for a simple regression. In this paper we derive a relation which relates the bias with the autocorrelation of disturbances and the autocorrelation of explanatory variables for a multiple regression with improved precision.

• 한국전자파학회논문지
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• 제19권2호
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• pp.130-137
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• 2008

본 논문은 레이다 표적 인식에서 레이다 산란신호에 대한 3차원 산란점 추출을 위한 고해상도 기법에 대해 기술하고 있다. 또한, 3차원 산란점 추출에서 신호의 극점을 획득하기 위해 3차원 짝 맞춤 절차를 소개하고 있다. 짝 맞춤 절차는 기존의 일반적인 방법보다 더 정확하고 견고한 특징을 가지고 있다. 3차원 산란점을 추출하기 위해서는 우선 주어진 3차원 레이다 산란 데이터로부터 상호 분산 행렬을 생성해야 한다. 그리고 matrix pencil 기법을 이용하여 3차원 산란점을 추출한다. 본 논문에서 MSSP를 이용하여 상호 분산 행렬을 생성하였으며, 관측 행렬은 sparse scanning order conception 방법을 이용하여 만들었다. 제시한 기법의 성능을 보여주기 위해서 본 논문에서는 이상적인 점 산란체를 생성하여 이에 대한 결과를 보여주고 있다.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• 풍력에너지저널
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• 제14권1호
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• 응용통계연구
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• 제28권2호
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• pp.211-219
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• 2015

일반화 선형혼합모델은 일반적으로 경시적 범주형 자료를 분석하는데 사용된다. 이 모델에서 임의효과는 반복 측정치들의 시간에 따른 의존성을 설명한다. 임의효과 공분산행렬의 추정은 여러가지 제약조건들 때문에 쉽지 않은 문제이다. 제약조건으로는 행렬의 모수들의 수가 많으며, 또한 추정된 공분산행렬은 양정치성을 만족하여야 한다. 이러한 제한을 극복하기 위해, 임의효과 공분산행렬의 모형화를 위한 여러가지 방법이 제안되었다: 수정 단냠레스키분해, 이동평균 단냠레스키분해와 부분 자기상관행렬을 이용한 방법이 있다. 이 논문에서 위의 제안된 방법들을 소개한다.

• Ocean and Polar Research
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• 제29권4호
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• pp.391-400
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• 2007

To identify the spatial distribution pattern of water quality in Masan Bay, Pearson's correlation as a common statistic method and Moran's I as a spatial autocorrelation statistics were applied to the hydrological data seasonally collected from Masan Bay for two years ($2004{\sim}2005$). Spatial distribution of salinity, DO and silicate among the hydrological parameters clustered strongly while chlorophyll a distribution displayed a weak clustering. When the similarity matrix of Moran's I was compared with correlation matrix of Pearson's r, only the relationships of temperature vs. salinity, temperature vs. silicate and silicate vs. total inorganic nitrogen showed significant correlation and similarity of spatial clustered pattern. Considering Pearson's correlation and the spatial autocorrelation results, water quality distribution patterns of Masan Bay were conceptually simplified into four types. Based on the simplified types, Moran's I and Pearson's r were compared respectively with spatial distribution maps on salinity and silicate with a strong clustered pattern, and with chlorophyll a having no clustered pattern. According to these test results, spatial distribution of the water quality in Masan Bay could be summed up in four patterns. This summation should be developed as spatial index to be linked with pollutant and ecological indicators for coastal health assessment.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• 전자공학회논문지B
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• 제32B권9호
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• pp.1188-1197
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• 1995

This paper describes an algorithm that reduces computational requirement of the Kalman filter and estimates the signal efficiently. The reference signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be smaller than that in the time domain. In the wavelet transform domain the autocorrelation matrix is nearly diagonal. Therefore, the transformed signal can be decomposed each orthogonal elements. The Kalman filter can be applied to each orthogonal elements and computational requirement is reduced. The possibility of applying the parallel Kalman filter was verified through the theory and simulation. The eigenvalue spread in the wavelet transform domain is smaller 8.35 times than that in the time domain and the computational requirement is reduced from 1.4 times to 2. 93 times than that of the conventional Kalman filter.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.112-118
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• 1994

The generalized least squares estimator in the linear regression model is equivalent to difference estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a seasonally autoregressive provess and autocorrelation is closed to unity.

• 한국음향학회지
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• 제15권6호
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• pp.12-15
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• 1996

실제 환경에서 배열은 비정상적으로 동작하는 소자를 가질 확률이 높다. 결함 소자는 출력이 없거나 정상인 것에 비해 크게 감소된 이득을 갖는다. 이는 빔의 부엽 레벨을 높이고, 적응 빔 형성기에서 간섭 신호를 제거하지 못하도록 한다. 이 논문에서는 결함 소자들을 갖는 배열을 위한 빔 형성 방법을 제안하였다. 이상적인 경우 배열 출력 자료들로부터 계산된 자기 상관 행렬은 Toeplitz 행렬이다. 그러나 결함 소자를 갖는 배열로부터 게산된 자기 상관 행렬은 결함 행렬이다. 따라서 이 행렬의 대각항들을 평균하여 얻어진 값들로 새로운 자기 상관 행렬을 구성하고, 새로 구성된 자기 상관 행렬을 빔형성 방법에 적용한다. 제안된 방법의 성능을 고찰하기 위해 컴퓨터 시뮬레이션을 수행하였다. 그 결과 제안된 방법은 기존의 부분 처리 기법의 단점이었던 자유도 문제를 해결할 수 있었다.